docs: def _extract_section(section):
      '''Find top level sections, and get their flat contents, and append them all
  
      Returns a numpy array with the row format:
          [X, Y, Z, R, TYPE, ID, PARENT_ID]
  
      Note: PARENT_ID starts at -1 for soma and 0 for neurites
      '''
      # sections with only one element will be skipped,
      if len(section) == 1:
          assert section[0] == 'Sections', \
              ('Only known usage of a single Section content is "Sections", found %s' %
               section[0])
          return None
  
      # try and detect type
      _type = WANTED_SECTIONS.get(section[0][0], None)
  
      start = 1
      # CellBody often has [['"CellBody"'], ['CellBody'] as its first two elements
      if _type is None:
          _type = WANTED_SECTIONS.get(section[1][0], None)
  
          if _type is None:  # can't determine the type
              return None
          start = 2
  
      parent = -1 if _type == POINT_TYPE.SOMA else 0
      subsection_iter = _flatten_subsection(section[start:], _type, offset=0,
                                            parent=parent)
  
      ret = np.array([row for row in subsection_iter])
      return ret